This section contains a brief summary of key SNOMED CT features and explains how they may be useful in CDSSs.
SNOMED CT concepts are used to represent clinical meanings. Every concept in SNOMED CT is uniquely identified by a distinct SNOMED CT Concept Identifier. For example, 195967001 is the concept identifier for the concept |Asthma|.
SNOMED CT concepts play an important role in CDS by enabling actions to be triggered based on the meaning of data recorded in the patient records.
SNOMED CT descriptions provide the human-readable terms associated with SNOMED CT concepts. A concept may have one or more descriptions, which act as synonyms for the same clinical meaning. This is also how SNOMED CT supports different dialects and languages.
SNOMED CT descriptions allow common CDS rules to be consistently applied across patient records recorded using different synonyms, dialects and languages.
SNOMED CT relationships link concepts together to formally define the meaning of each concept. For example, one type of relationship is the |is a| relationship which relates a concept to a parent or supertype. These |is a| relationships define the subtype hierarchy of SNOMED CT concepts.
For example, the concepts |Bacterial pneumonia| and |Viral pneumonia| both have an |is a| relationship to |Infective pneumonia| which has an |is a| relationship to the more general concept |Pneumonia|. Subtype relationships can be used by CDS rules to refer to codes in an EHR that are any specific type of a relevant clinical concept.
Additional attribute relationships help to define the meaning of a concept. For example, the concept |Viral pneumonia| has a |Causative agent| relationship to the concept |Virus| and a |Finding site| relationship to the concept |Lung structure|.
Attribute relationships can be used by CDS rules to refer to codes recorded in an EHR that have a specific meaningful relationship with a concept of interest.
The SNOMED CT concept model is a set of rules that govern the ways in which SNOMED CT concepts are permitted to be modeled using relationships to other concepts. It defines the types of relationships that may be used on each type of concepts, and the permitted values for each relationship type. The Machine Readable Concept Model (MRCM) represents the rules in the SNOMED CT concept model in a form that can be read by a computer and applied to test that concept definitions and expressions comply with these rules.
The SNOMED CT concept model plays an important role in CDS by providing the rules by which the clinical meaning of SNOMED CT encoded health records can be queried. The MRCM makes it possible to process these rules in a machine-processable way.
SNOMED CT provides a mechanism which enables clinical phrases to be represented by a computable expression, when a single concept does not capture the necessary level of detail. For example, the following expression represents a right hip:
SNOMED CT expressions facilitate CDS over an expanded set of clinical meanings that extends beyond individual concepts. For more information about expressions, please refer to the Compositional Grammar - Specification and Guide.
SNOMED CT reference sets are a flexible and standardized approach used to support a variety of requirements for the customization and enhancement of SNOMED CT. These include the representation of subsets, language preferences for use of particular terms, mapping from or to other code systems, and ordered lists.
Reference sets may be used in the following aspects of CDS:
- Representing subsets of SNOMED CT concepts that may trigger a CDS action
- Representing non-standard aggregations of concepts for specific CDS use cases
- Defining language or dialect specific sets of descriptions over which term searches can be performed
For more information about reference sets, please refer to the Practical Guide to Reference Sets.
Description Logic Features
Description Logic (DL) is a family of formal knowledge representation languages and used as the formal foundation of meaning in SNOMED CT. The way that concepts have been modeled in SNOMED CT permits them to be represented using Description Logic. DL helps computers to make useful inferences about concepts, and to classify SNOMED CT using a DL reasoner. Description Logic also helps by testing expressions for subsumption and equivalence.
The logical inferences supported by DL can be useful when executing CDS rules. For example, when a CDS rule requires an action to be performed when the patient has any type of |Asthma|, a DL reasoner may be used to determine that |Acute asthma| and |Intermittent asthma| are both types of |Asthma| and should therefore both trigger the action to be performed.